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this is chill Tao from the SAS technical support statistics group todays presentation will help you understand the subject equal effect in SAS mixed models software I will start with models with one subject effect examples will include a random intercept model and the random intercept and slope model this will lead to a closer look at the G matrix then I will talk about models with more than one subject effect issues include whether you should specify the subject effects as crossed or nested how do you specify the models so they are more numerically efficient and what it means to make the model processed by subjects finally I will use a model with both random and repeated statements to further explore the subject effect and now well explain the R matrix as well lets begin with this simple example which consists of data from several hospitals each hospital has multiple patients and each patient has four measurements of X or kobarid and y the response variable lets ignore the hospit
